title: Identifying relevant data for a biological database: handcrafted rules versus machine learning.
abstract: With well over 1,000 specialized biological databases in use today, the task of automatically identifying novel, relevant data for such databases is increasingly important. In this paper, we describe practical machine learning approaches for identifying MEDLINE documents and Swiss-Prot/TrEMBL protein records, for incorporation into a specialized biological database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both the methods compared and the results will be of interest to curators of many specialized databases.
authors.affiliation: Core Technologies Group, Parity Computing, San Diego, CA 92121, USA. a.sehgal@paritycomputing.com
authors.forename: Aditya Kumar
authors.initials: AK
authors.lastname: Sehgal
authors.forename: Sanmay
authors.initials: S
authors.lastname: Das
authors.forename: Keith
authors.initials: K
authors.lastname: Noto
authors.forename: Milton H
authors.initials: MH
authors.lastname: Saier
authors.forename: Charles
authors.initials: C
authors.lastname: Elkan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002352:Carrier Proteins; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D023281:Genomics / Q000379:methods*; D006801:Humans; D016239:MEDLINE; D011506:Proteins / Q000145:classification / Q000235:genetics
chemical_list: D002352:Carrier Proteins; D011506:Proteins